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Table 1 Examples of a wide variety of projects using big data in psychiatry

From: Big data are coming to psychiatry: a general introduction

Description Primary finding Number of subjects (n) Data source References
Create actuarial suicide risk algorithm to predict suicide in the 12 months after inpatient hospitalization for psychiatric disorder 52.9 % of posthospitalization suicides occurred after the 5 % of hospitalizations with the highest predicted suicide risk 40,820 soldiers hospitalized for psychiatric disorders. 421 predictors 38 army and DOD administrative data Kessler et al. (2015)
Explore prevalence of substance use disorders (SUD) among psychiatric patients in large university system 24.9 % of patients had SUD; SUD associated with more inpatient and emergency care 40,999 psychiatric patients aged 18–64 years who sought treatment between 2000 and 2010 EMR-based psychiatry registry Wu et al. (2013)
Ongoing study of cognitive impairment using neuroimaging and genetics Neuroimaging phenotypes were significantly associated with progression of dementia 808 patients over age 65, including 200 with Alzheimer’s disease 20 derived neuroimaging markers plus 20 SNPs Weiner et al. (2012)
Examine use of psychotropic drugs by patients without psychiatric diagnosis 58 % of those prescribed a psychiatric medication in 2009 had no psychiatric diagnosis 5,132,789 individuals who received prescription for psychotropic medication Private medication claims database Wiechers et al. (2013)
Analyze prescribing of psychotropic drugs by specialty 59 % written by general practitioners, 23 % by psychiatrists, 17 % by other physicians and providers 472 million prescriptions for psychotropic drugs IMS database of 70 % of US retail pharmacy transactions for 2006–2007 Mark et al. (2009)
Compare risk of dementia in those 55 or older having traumatic (TBI) brain injury versus non-TBI trauma (NTT) TBI increased risk for dementia over NTT 51,799 patients with trauma, of which 31.5 % had TBI CA statewide administrative health database of ER and inpatient visits Gardner et al. (2014)
Use machine learning to predict suicidal behavior text in EMR Model obtained high specificity but low sensitivity, with PPV of 41 % 250,000 US veterans of Gulf War Clinical records Ben-Ari and Hammond (1991)
Investigate association between maternal and paternal age and risk of autism Both increasing maternal age and increasing paternal age were independently associated with increased risk of autism 7,550,026 single births in CA 1989–2002. 23,311 with autism Developmental services administrative data, birth certificate data Grether et al. (2009)
Use natural language processing (NLP) to classify current mood state to identify treatment resistant depression NLP models better than those relying on billing data alone 127,504 patients with diagnosis of major depression EMR and billing data from outpatient psychiatry practices affiliated with large hospital Perlis et al. (2012)
Analyze impact of Medicaid prior authorization for atypical antipsychotics on prevalence of schizophrenia among prison inmates Prior authorization associated with greater prevalence of mental illness in inmates 16,844 inmates Nationally representative sample from Census Bureau Goldman et al. (2014)
Investigate incidence of severe psychiatric disorders following hospital contact for head injury Increased risk of schizophrenia, depression, bipolar disorder and organic mental disorders following head injuries 113,906 people who had suffered head injuries, and were born between 1977 and 2000 Danish psychiatric central register Orlovska et al. (2014)
Integrate depression screening, prescription fulfillment and EMR to improve care in primary care (PC) Integration improved diagnosis and management of depression in PC 61,464 patients in PC in 14 clinical organizations EMR, plus 4900 PHQ-9 questionnaires, plus fulfillment data for 55 % of patients Valuck et al. (2012)
Analyze if SSRI/SNRI use prior to admission to ICU increased mortality risk Increased hospital morality among those in ICU taking SSRI/SNRI before admission 14,709 patients with 2471 taking SSRI/SNRI Multiparameter Intelligent Monitoring in Intensive Care database (data from EMR) Ghassemi et al. (2014)
Evaluate safety of antipsychotic (AP) medication use in nursing homes Dose-dependent increased risks of serious medical events such as myocardial infarction, stroke, infection, hip fracture, within 180 days of initiating AP treatment 83,959 Medicaid eligible residents ≥age 65 who initiated AP use after nursing home admission Medicare and Medicaid claims from 45 states Huybrechts et al. (2012)
Evaluate use of EMR to assist with phenotyping in bipolar disorder (BP) Semiautomated data mining of EHR may assist with phenotyping of patients and controls 52,235 patients with at least one diagnosis of BP or mania, spanning 20 years EMR, billing and inpatient pharmacy data Castro et al. (2015)